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Pitch Period and Pitch Frequency Estimation Environment in Scilab

Chetan Solanki, Chhaya Kinkar

Abstract


Speech is one of the natural forms of communication. Speech processing is emerged as one of the important application area of digital signal processing. Various fields for research in speech processing are speech recognition, speaker recognition, speech synthesis, speech coding etc. The objective of speech recognition is to extract, characterize and recognize the information about speaker identity. Pitch estimation is the first step for speech recognition. Many algorithms are suggested/developed by the researchers for pitch estimation. In this work, pitch frequency and pitch period estimation algorithm has been used for determining pitch frequency and pitch period of different person. In Speech recognition certain words of a particular speaker will automatically recognize. In this paper we presented the result an experiment which is perform on 5 different persons to recognize the individual one. Recognition platform is implemented on scilab 5.3.3 for recognition purpose pitch period or individual person is calculated. This technique is one of the most useful and popular biometric recognition techniques in the world especially related to areas in which security is a major concern. It can be used for authentication, surveillance, speaker recognition and a number of related activities.


Keywords


Pitch Period, Pitch Frequency, Voiced and Unvoiced Sound, Coding, Text Dependent, Text Independent.

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References


Fu Zhonghua: Zhao; “An overview of modeling technology of speaker recognition”, IEEE Proceedings of the International Conference on Neural Networks and Signal Processing Volume 2, Page(s):887 – 891, Dec. 2003.

Mauge Sundermeyer, Ralf Schliter, Hermann Ney, On the Estimation of discount parameters for language model smoothing” Human language technology. earn recognisation cs dept, RWTH Aachen University

E. Verbitskiy, P. Tuyls, D. Denteneer, and J. P. Linnartz, “Reliable biometric authentication with privacy protection,” presented at the SPIE Biometric Technology for Human Identification Conf., Orlando, FL, 2004.

Francesco Bergadano, Daniele Gunetti, and Claudia Picardi. User authentication through keystroke dynamics. ACM Trans. Inf. Syst. Secur., 5(4):367–397, 2002.

Cenker Oden, Vedat Taylan Yildiz, Hikmet Kirmizitas, and Burak Buke. Hand recognition using implicit polynomials and geometric features. In AVBPA ’01: Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication, pages 336–341, London, UK, 2001. Springer-Verlag.

Ajay Kumar and David Zhang. Integrating shape and texture for hand verification. In ICIG ’04: Proceedings of the Third International Conference on Image and Graphics (ICIG’04), pages 222–225, Washington, DC, USA, 2004.IEEE Computer Society.

Roucos, S. Berouti, M. Bolt, Beranek and Newman, Inc., Cambridge, MA; “The application of probability density estimation to text-independent speaker identification” IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '82. Volume: 7, On page(s): 1649- 1652. Publication Date: May 1982.

Castellano, P.J., slomka, S. Sriram, S.; “Telephone based speaker recognition using multiple binary classifier and Gaussian mixture models” IEEE International Conference on Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 Volume 2, Page(s) 1075 – 1078 April 1997.

Zoltan Tuseke,Pavel Golik,Ralf Schlliter,Friendhelum R Drepper”Non Staionary feature extraction for automatic speech recognisation” ” Human language technology and patt. Ern recognisation cs dept,RWTH Aachen University.

Philipp Koehn and Jean Senellart, ”Convergence of Translation Memory and Statistical Machine Translation”, AMTA Workshop on MT Research and the Translation Industry, 2010.

Yong Lu, Haining Huang “Research on a kind of Noisy Tibetan speech recognition algorithm based on WNN” 2011 IEEE.

Paper on “Study of speaker recognition system” by ashish panda, amit kumar sahoo thesis, NIIT ROURKELA 2011.

Yong Lu, Haining Huang “Research on a kind of Noisy Tibetan speech recognition algorithm based on WNN” 2011 IEEE.

MohdSyahrizad Bin Elias, “Speaker Recognition Using Enhanced MFCC”, university of UTARA, Malaysia, 2009.

M. Kudinov “Comparison of Some Algorithms for Endpoint Detection for Speech Recognition Device Used in Cars” 2011 IEEE. Paper on “speech recognition based system to control electrical appliances” by Arvinder singh and Gagandeep singh in IJEIT volume 2 issue 1 august 2012.


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